With rapid economic and social developments, traffic problems have become increasingly severe in urban areas. Traffic jams and congestions greatly affect people's traveling. The increasing amount of time spent on daily commutes also severely reduces production efficiency and work efficiency for the society as a whole, and affects people's happiness index.
Existing technologies seek to address the foregoing problems by using methods for extracting real-time urban traffic flow data based on positioning data of a mobile phone. One such method includes: preprocessing positioning data of a mobile phone and map data; determining candidate matching road sections of the positioning data and a roughly-chosen matching point set of each road section; extracting vehicle-mounted mobile phone data, i.e., determining a finely-chosen matching point set of each road section; and calculating an average space velocity for the road sections by using a weighted average velocity method.
The existing methods of extracting real-time urban traffic flow data based on positioning data of a mobile phone has a number of disadvantages, which include complex data extraction and processing, difficulty in actual operation and deployment, and high implementation costs. For example, the disadvantages are manifested in the following aspects.
With the existing methods, it is required to use an electronic map as an input to extract urban road network information. Further, the methods require establishing a storage unit for each road section for storing its road section serial number, road section direction, road section function grade, and road section space data. The methods also require selecting matching point sets for the road sections. The process involves a significant amount of data processing and complex calculations, and the operations are not suitable for automatic execution.
Further, in the existing technology, it is required to extract vehicle-mounted mobile phone data to determine a finely-chosen matching point set of each road section and estimate an average space velocity of the road section by using a space velocity weighted method. Unless the vehicle-mounted mobile phones are deployed manually for data collection, the source of positioning data needs to be verified, which affects applicability of the methods. In addition, it is extremely difficult to continuously acquire precise positioning data of an individual mobile phone. Therefore, the foregoing methods, although operable theoretically, are difficult to apply in practice.
In view of the above, existing methods of extracting urban traffic flow data based on mobile phone positioning data have the problems of low efficiency in determining popularity of traffic routes, due to complex traffic data extraction and processing operations. There is a need for effective solutions to solve the problems.